AI Reports
Review AI-predicted customer satisfaction (CSAT), sentiment analysis, emotion detection, topic categorization, and quality insights to understand your customers' experience.
The AI tab contains 23 reports powered by AI analysis of your conversations. These reports provide insights into customer satisfaction, sentiment, emotions, and conversation topics without requiring manual surveys or tagging.
NoteAI reports use machine learning predictions based on conversation content. Only conversations with medium or high confidence scores are included in the results.
Customer Satisfaction (CSAT)
Track AI-predicted customer satisfaction scores across your organization.
CSAT Score Distribution
Shows the distribution of AI-predicted satisfaction scores on a 1–5 scale.
| Metric | Description |
|---|---|
| CSAT Score | Score from 1 (very dissatisfied) to 5 (very satisfied) |
| Count | Number of conversations with this score |
| Percentage | Share of total rated conversations |
| Average CSAT | Overall average satisfaction score |
Use this report to:
- Understand overall satisfaction levels
- Track improvements over time
- Set benchmarks for your team
CSAT by Agent
Compares AI-predicted satisfaction scores across agents.
| Metric | Description |
|---|---|
| Agent | Agent name |
| Rated Chats | Conversations with CSAT scores |
| Avg CSAT | Average satisfaction score |
| Satisfied % | Percentage scoring 4 or 5 |
| Dissatisfied % | Percentage scoring 1 or 2 |
Use this report to:
- Compare performance across agents
- Identify top performers
- Spot agents who may need coaching
- Set individual satisfaction goals
A minimum number of conversations is required for a reliable comparison. Agents with very few rated conversations should be interpreted with caution.
CSAT by Channel
Compares AI-predicted satisfaction scores across communication channels.
Shows Channel, Type, Rated conversations, Average CSAT, and Satisfied percentage for each channel.
Use this report to:
- Identify which channels deliver the best customer experience
- Spot channels needing improvement
- Prioritize channel investments
- Compare satisfaction across platforms
CSAT by Business Unit
Compares AI-predicted satisfaction scores across Business Units.
Shows BU name, Rated conversations, Average CSAT, Satisfied percentage, and Dissatisfied percentage.
Use this report to:
- Compare team satisfaction performance
- Identify high-performing teams
- Spot teams that may need additional support
- Set team-level satisfaction targets
CSAT by Country
Shows AI-predicted satisfaction scores by customer country.
Displays Country, Conversations, Average CSAT, Satisfied %, Neutral %, and Dissatisfied % for each country.
Use this report to:
- Identify low-satisfaction regions
- Compare service quality across markets
- Track regional satisfaction initiatives
- Prioritize resources for underperforming regions
CSAT Trend
Tracks AI-predicted satisfaction changes day by day as a trend line.
Shows Date, Average CSAT, Rated conversations, and Satisfied percentage over time.
Use this report to:
- Monitor satisfaction trends over time
- Correlate changes with product updates or process changes
- Identify the impact of training or staffing changes
- Track progress toward satisfaction goals
Sentiment Analysis
Understand the overall mood of your customer conversations.
Sentiment Distribution
Shows the breakdown of Positive, Neutral, and Negative sentiment across conversations.
Displays each sentiment category with its count and percentage of total conversations.
Use this report to:
- Get an overview of customer mood
- Track sentiment trends between periods
- Set baselines for improvement initiatives
Sentiment by Agent
Shows sentiment breakdown (Positive, Neutral, Negative) per agent with a composite Score.
| Metric | Description |
|---|---|
| Agent | Agent name |
| Chats | Total conversations |
| Positive % | Percentage with positive sentiment |
| Negative % | Percentage with negative sentiment |
| Score | Composite score: (Positive - Negative) / Total × 100 |
Use this report to:
- Identify agents who consistently generate positive outcomes
- Spot agents with high negative sentiment for coaching
- Compare agent communication styles
Neutral conversations pull the score toward zero. A high neutral percentage is normal and not a concern.
Sentiment by Channel
Shows sentiment breakdown for each communication channel.
Displays Channel, Type, Chats, Positive %, and Negative % per channel.
Use this report to:
- Understand which channels produce the most positive interactions
- Identify channels with frustration issues
- Compare customer mood across platforms
Sentiment by Country
Shows sentiment breakdown by customer country.
Displays Country, Chats, Positive %, Neutral %, Negative %, and Score.
Use this report to:
- Understand regional sentiment differences
- Identify frustrated regions
- Compare customer mood across markets
- Prioritize regional improvements
Sentiment by Hour
Shows customer sentiment patterns by hour of day (UTC).
| Metric | Description |
|---|---|
| Hour | Hour of day (UTC) |
| Chats | Number of conversations |
| Positive % | Positive sentiment percentage |
| Negative % | Negative sentiment percentage |
| Score | Composite sentiment score (-100 to +100) |
Use this report to:
- Identify hours with higher negative sentiment
- Correlate sentiment with staffing levels
- Plan coverage to improve sentiment during problem hours
Sentiment Trend
Shows the proportion of Positive, Neutral, and Negative sentiment over time as a trend chart.
Displays Date, Positive %, Neutral %, Negative %, and Score for each day.
Use this report to:
- Monitor sentiment trends over time
- Identify unusual patterns or sentiment shifts
- Correlate sentiment changes with events or process updates
- Track improvement initiatives
Emotion Analysis
Detect specific emotions in your customer conversations for deeper insight beyond positive/negative sentiment.
Emotion Distribution
Shows specific emotions detected in conversations, such as Gratitude, Impatience, Frustration, and others.
Displays each emotion with its count and percentage of total conversations.
Use this report to:
- Understand the emotional context of customer interactions
- Identify frustration triggers
- Recognize gratitude and positive engagement
- Guide emotional intelligence training
Emotion by Category
Shows which emotions are detected within each topic category — revealing which issues trigger which emotional responses.
Displays Category, Total conversations, and the Dominant Emotion for each topic.
Use this report to:
- Understand which topics trigger frustration
- Identify gratitude-generating topics
- Guide process improvements for high-frustration categories
- Tailor agent training by topic and emotion
Emotion Trend
Tracks specific emotion frequency over time (Gratitude, Impatience, etc.) as a multi-line chart.
Use this report to:
- Monitor emotional patterns over time
- Identify periods of increased frustration
- Track whether gratitude increases after improvements
- Correlate emotional trends with events or changes
Category & Topic Analysis
Understand what your customers are contacting you about through AI-detected topic categories.
Category Volume
Shows conversation volume by AI-detected topic category.
| Column | Description |
|---|---|
| Category | AI-assigned topic |
| Conversations | Number of conversations in this category |
| Avg CSAT | Average satisfaction for this category |
| Negative % | Percentage with negative sentiment |
Use this report to:
- Identify common inquiry types
- Spot categories with low satisfaction
- Prioritize training for high-volume topics
- Track topic patterns over time
Category by Agent
Shows which AI-assigned categories each agent handles most, revealing specialization patterns.
Displays Agent, total category tags, and the Top Category for each agent.
Use this report to:
- Identify agent specializations
- Plan skill-based routing
- Understand workload distribution by topic
- Guide agent training by area of expertise
Total category tags can exceed the chat count because a single conversation may be assigned multiple categories.
Category by Country
Shows what topics customers discuss, grouped by country. Different markets often have different support needs.
Results are grouped by country, showing the top 5 categories per country with count and percentage.
Use this report to:
- Understand regional product or service differences
- Plan localized FAQ and self-service content
- Identify country-specific issues
- Tailor agent training by region
Quality Insights
Advanced analysis to identify root causes of dissatisfaction and measure AI effectiveness.
Agent Sentiment Ranking
Ranks agents by overall sentiment score in a leaderboard format.
| Column | Description |
|---|---|
| Rank | Position in the leaderboard |
| Agent | Agent name |
| Chats | Total conversations |
| Positive % | Positive sentiment percentage |
| Negative % | Negative sentiment percentage |
| Score | Composite sentiment score |
Use this report to:
- Recognize top-performing agents
- Identify agents needing support
- Create healthy competition
- Track ranking changes over time
A minimum of 10 conversations is required for an agent to be included in the ranking.
Dissatisfaction Drivers
Identifies which topic categories are most associated with customer dissatisfaction.
| Column | Description |
|---|---|
| Category | Topic category |
| Dissatisfied | Number of dissatisfied conversations |
| Avg CSAT | Average satisfaction for this category |
| Avg Wait | Average wait time for these conversations |
| Avg Response | Average response time |
Use this report to:
- Identify improvement priorities
- Understand correlation between wait times and dissatisfaction
- Guide investment decisions
- Focus training on high-dissatisfaction topics
Low CSAT Root Cause
Multi-dimensional analysis of conversations with low CSAT scores (1–2) to identify root causes of dissatisfaction.
The report includes multiple sections:
- By Category — Which topics generate low satisfaction
- By Emotion — Which emotions are present in dissatisfied conversations
- By Agent — Which agents have the most low-CSAT conversations
- By Channel — Which channels have more dissatisfied customers
- By Hour — When low-CSAT conversations occur
- AI Reasons — Sample AI-generated explanations for low scores
Use this report to:
- Conduct comprehensive root cause analysis
- Identify the most impactful improvements
- Understand the full picture of dissatisfaction
- Prioritize initiatives with the highest potential impact
AI Tag Analysis
Shows AI-assigned tag usage statistics across your conversations.
The report includes sections for Category Tags, Emotion Tags, and overall Totals showing how many conversations have tags, total category tag occurrences, and total emotion tag occurrences.
Use this report to:
- Review AI categorization coverage
- Identify the most common tags
- Validate AI accuracy
- Plan tag taxonomy improvements
AI Bot Containment
Measures how effectively AI agents resolve customer conversations without transferring to human agents.
| Metric | Description |
|---|---|
| Total AI Chats | Conversations handled by AI agents |
| Contained | Resolved by AI without human transfer |
| Transferred | Escalated to a human agent |
| Containment Rate % | Percentage resolved entirely by AI |
The report also includes a breakdown by channel and a daily containment trend chart.
Use this report to:
- Evaluate AI bot efficiency and ROI
- Identify channels where AI performs best
- Track containment improvements over time
- Determine what percentage of volume needs human agents
A chat is considered "contained" when the AI agent resolves it without transferring to a human agent.